
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% FairCal idk what more could be in this section %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%%% Faircal paper
@inproceedings{
  salvador2022faircal,
  title={FairCal: Fairness Calibration for Face Verification},
  author={Tiago Salvador and Stephanie Cairns and Vikram Voleti and Noah Marshall and Adam M Oberman},
  booktitle={International Conference on Learning Representations},
  year={2022},
  url={https://openreview.net/forum?id=nRj0NcmSuxb}
}

%%% FairCal github
@misc{salvador_2021,
  title={Code for the paper FairCal: Fair calibration for face verification.},
  url={https://github.com/tiagosalvador/faircal},
  journal={GitHub},
  author={Salvador, Tiago},
  year={2021},
  month={11}
}


%%%%%%%%%%%%%%%%%%%%%%%%
%%% Fairness metrics %%%
%%%%%%%%%%%%%%%%%%%%%%%%

%%% AUROC fairness score
@inproceedings{jesse2006auroc,
  author = {Davis, Jesse and Goadrich, Mark},
  title = {The Relationship between Precision-Recall and ROC Curves},
  year = {2006},
  isbn = {1595933832},
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  url = {https://doi.org/10.1145/1143844.1143874},
  doi = {10.1145/1143844.1143874},
  abstract = {Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.},
  booktitle = {Proceedings of the 23rd International Conference on Machine Learning},
  pages = {233–240},
  numpages = {8},
  location = {Pittsburgh, Pennsylvania, USA},
  series = {ICML '06}
}


%%%%%%%%%%%%%%%%
%%% Datasets %%%
%%%%%%%%%%%%%%%%

%%% VGGFace2 dataset
@INPROCEEDINGS{cao2018vggface2,
  author={Cao, Qiong and Shen, Li and Xie, Weidi and Parkhi, Omkar M. and Zisserman, Andrew},
  booktitle={2018 13th IEEE International Conference on Automatic Face \& Gesture Recognition (FG 2018)}, 
  title={VGGFace2: A Dataset for Recognising Faces across Pose and Age}, 
  year={2018},
  volume={},
  number={},
  pages={67-74},
  doi={10.1109/FG.2018.00020}
}


%%% CASIA-Webface dataset
@article{DBLP:journals/corr/YiLLL14awebface,
  author    = {Dong Yi and
               Zhen Lei and
               Shengcai Liao and
               Stan Z. Li},
  title     = {Learning Face Representation from Scratch},
  journal   = {CoRR},
  volume    = {abs/1411.7923},
  year      = {2014},
  url       = {http://arxiv.org/abs/1411.7923},
  eprinttype = {arXiv},
  eprint    = {1411.7923},
  timestamp = {Tue, 12 Apr 2022 15:00:34 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/YiLLL14a.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}


%%% MS-Celeb-1M dataset
@InProceedings{10.1007/978-3-319-46487-9_6msceleb1,
  author="Guo, Yandong and Zhang, Lei and Hu, Yuxiao and He, Xiaodong and Gao, Jianfeng",
  editor="Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max",
  title="MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition",
  booktitle="Computer Vision -- ECCV 2016",
  year="2016",
  publisher="Springer International Publishing",
  address="Cham",
  pages="87--102",
  abstract="In this paper, we design a benchmark task and provide the associated datasets for recognizing face images and link them to corresponding entity keys in a knowledge base. More specifically, we propose a benchmark task to recognize one million celebrities from their face images, by using all the possibly collected face images of this individual on the web as training data. The rich information provided by the knowledge base helps to conduct disambiguation and improve the recognition accuracy, and contributes to various real-world applications, such as image captioning and news video analysis. Associated with this task, we design and provide concrete measurement set, evaluation protocol, as well as training data. We also present in details our experiment setup and report promising baseline results. Our benchmark task could lead to one of the largest classification problems in computer vision. To the best of our knowledge, our training dataset, which contains 10M images in version 1, is the largest publicly available one in the world.",
  isbn="978-3-319-46487-9",
  url={https://doi.org/10.1007/978-3-319-46487-9_6}
}


%%% RFW
@InProceedings{Wang_2019_ICCVrfw1,
  author = {Wang, Mei and Deng, Weihong and Hu, Jiani and Tao, Xunqiang and Huang, Yaohai},
  title = {Racial Faces in the Wild: Reducing Racial Bias by Information Maximization Adaptation Network},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
  month = {10},
  year = {2019}
}
@article{wang2021metarfw2,
  title={Meta Balanced Network for Fair Face Recognition},
  author={Wang, Mei and Zhang, Yaobin and Deng, Weihong},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
  publisher={IEEE}
}
@article{wang2019skewnessrfw3,
  title={Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning},
  author={Wang, Mei and Deng, Weihong},
  journal={arXiv preprint arXiv:1911.10692},
  year={2019}
}
@article{wang2018deeprfw4,
  title = {Deep Face Recognition: A survey},
  author = {Mei Wang and Weihong Deng},
  journal = {Neurocomputing},
  volume = {429},
  pages = {215-244},
  year = {2021}
}


%%% BFW
@article{DBLP:journals/corr/abs-2002-06483bfw,
  author    = {Joseph P. Robinson and
               Gennady Livitz and
               Yann Henon and
               Can Qin and
               Yun Fu and
               Samson Timoner},
  title     = {Face Recognition: Too Bias, or Not Too Bias?},
  journal   = {CoRR},
  volume    = {abs/2002.06483},
  year      = {2020},
  url       = {https://arxiv.org/abs/2002.06483},
  eprinttype = {arXiv},
  eprint    = {2002.06483},
  timestamp = {Mon, 02 Mar 2020 16:46:06 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2002-06483.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

%%%%%%%%%%%%%%
%%% Models %%%
%%%%%%%%%%%%%%

%%% FaceNet model
%%% FaceNet Webface model
%%% Facenet_pytorch
@misc{facenetpytorch,
  title={Face recognition using pytorch},
  author={Tim Esler and Contributors},
  journal={GitHub},
  url={https://github.com/timesler/facenet-pytorch},
  year={2021},
  month={12}
}


%%% Inception model v1.
@InProceedings{Schroff_2015_CVPRinception,
  author = {Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},
  title = {FaceNet: A Unified Embedding for Face Recognition and Clustering},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {6},
  year = {2015}
}

%%% Inception model referenced from faircal paper, but isn't this too new a version and it's actually using v1?
@article{Szegedy_Ioffe_Vanhoucke_Alemi_2017,
  title={Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning},
  volume={31},
  url={https://ojs.aaai.org/index.php/AAAI/article/view/11231},
  DOI={10.1609/aaai.v31i1.11231},
  abstractNote={ Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08\% top-5 error on the test set of the ImageNet classification (CLS) challenge.},
  number={1},
  journal={Proceedings of the AAAI Conference on Artificial Intelligence},
  author={Szegedy, Christian and Ioffe, Sergey and Vanhoucke, Vincent and Alemi, Alexander},
  year={2017},
  month={2}
}


%%% ArcFace model
@misc{salvador_2021arcface,
  title={ONNX Model Zoo},
  url={https://github.com/onnx/models},
  journal={GitHub},
  author={Open Neural Network Exchange (ONNX)},
  year={2022},
  month={12}
}


%%% ResNet100
@InProceedings{He_2016_CVPRresnet100,
  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  title = {Deep Residual Learning for Image Recognition},
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {6},
  year = {2016}
}



%%% MTCNN
@article{zhang2016jointmtcnn,
  title={Joint face detection and alignment using multitask cascaded convolutional networks},
  author={Zhang, Kaipeng and Zhang, Zhanpeng and Li, Zhifeng and Qiao, Yu},
  journal={IEEE signal processing letters},
  volume={23},
  number={10},
  pages={1499--1503},
  year={2016},
  publisher={IEEE}
}


%%% FSN
@article{terhorst2020post,
  title={Post-comparison mitigation of demographic bias in face recognition using fair score normalization},
  author={Terh{\"o}rst, Philipp and Kolf, Jan Niklas and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan},
  journal={Pattern Recognition Letters},
  volume={140},
  pages={332--338},
  year={2020},
  publisher={Elsevier}
}

%%% FTC
@inproceedings{terhorst2020comparison,
  title={Comparison-level mitigation of ethnic bias in face recognition},
  author={Terh{\"o}rst, Philipp and Tran, Mai Ly and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan},
  booktitle={2020 8th international workshop on biometrics and forensics (iwbf)},
  pages={1--6},
  year={2020},
  organization={IEEE}
}



%%% beta calibration

@inproceedings{patel2021multiclass-betacalibration,
  title={Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning},
  author={Kanil Patel and William H. Beluch and Bin Yang and Michael Pfeiffer and Dan Zhang},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=AICNpd8ke-m}
}

%%% Kolmogorov-Smirnov (KS) calibration error
@inproceedings{gupta2021calibration,
  title={Calibration of Neural Networks using Splines},
  author={Kartik Gupta and Amir Rahimi and Thalaiyasingam Ajanthan and Thomas Mensink and Cristian Sminchisescu and Richard Hartley},
  booktitle={International Conference on Learning Representations},
  year={2021},
  url={https://openreview.net/forum?id=eQe8DEWNN2W}
}





%%% scipy

@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
          and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
          and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
          Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}


%%% numpy

@Article{         harris2020array,
 title         = {Array programming with {NumPy}},
 author        = {Charles R. Harris and K. Jarrod Millman and St{\'{e}}fan J.
                 van der Walt and Ralf Gommers and Pauli Virtanen and David
                 Cournapeau and Eric Wieser and Julian Taylor and Sebastian
                 Berg and Nathaniel J. Smith and Robert Kern and Matti Picus
                 and Stephan Hoyer and Marten H. van Kerkwijk and Matthew
                 Brett and Allan Haldane and Jaime Fern{\'{a}}ndez del
                 R{\'{i}}o and Mark Wiebe and Pearu Peterson and Pierre
                 G{\'{e}}rard-Marchant and Kevin Sheppard and Tyler Reddy and
                 Warren Weckesser and Hameer Abbasi and Christoph Gohlke and
                 Travis E. Oliphant},
 year          = {2020},
 month         = sep,
 journal       = {Nature},
 volume        = {585},
 number        = {7825},
 pages         = {357--362},
 doi           = {10.1038/s41586-020-2649-2},
 publisher     = {Springer Science and Business Media {LLC}},
 url           = {https://doi.org/10.1038/s41586-020-2649-2}
}

%%% pandas

@software{the_pandas_development_team_2022_7344967,
  author       = {The pandas development team},
  title        = {pandas-dev/pandas: Pandas},
  month        = nov,
  year         = 2022,
  note         = {If you use this software, please cite it as below.},
  publisher    = {Zenodo},
  version      = {v1.5.2},
  doi          = {10.5281/zenodo.7344967},
  url          = {https://doi.org/10.5281/zenodo.7344967}
}
@InProceedings{ mckinney-proc-scipy-2010,
  author    = { {W}es {M}c{K}inney },
  title     = { {D}ata {S}tructures for {S}tatistical {C}omputing in {P}ython },
  booktitle = { {P}roceedings of the 9th {P}ython in {S}cience {C}onference },
  pages     = { 56 - 61 },
  year      = { 2010 },
  editor    = { {S}t\'efan van der {W}alt and {J}arrod {M}illman },
  doi       = { 10.25080/Majora-92bf1922-00a }
}


%%% scipy
% https://scipy.org/citing-scipy/

@ARTICLE{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}

















%%%%% Already entered by template

@article{Rougier:2018,
  doi = {10.1038/d41586-018-04628-w},
  year = {2018},
  month = apr,
  publisher = {Springer Nature},
  volume = {556},
  number = {7701},
  pages = {309--309},
  author = {Rougier, Nicolas P. and Hinsen, Konrad},
  title = {Code reviewing puts extra demands on referees },
  journal = {Nature},
}

@article{Science:2018,
  doi = {10.1126/science.359.6377.725},
  author = {Hutson, Matthew},
  url = {http://science.sciencemag.org/content/359/6377/725},
  year = {2018},
  month = feb,
  volume = {359},
  number = {6377},
  title = {Artificial intelligence faces a replication crisis},
  journal = {Science},
}

@Misc{Roboto:2011,
  author =       {Christian Robertson},
  title =        {The Roboto family of fonts (Google)},
  url =          {https://github.com/google/roboto},
  year =         2011,
  note =         {Apache License, verison 2.0},
}

@Misc{SourceSerifPro:2014,
  author =       {Frank Grießhammer},
  title =        {Source Serif Pro (Adobe Systems)},
  url =          {https://github.com/adobe-fonts/source-serif-pro},
  year =         2014,
  note =         {SIL Open Font License, version 1.1},
}

@Misc{SourceCodePro:2012,
  author =       {Paul D. Hunt},
  title =        {Source Code Pro (Adobe Systems)},
  url =          {https://github.com/adobe-fonts/source-code-pro},
  year =         2012,
  note =         {SIL Open Font License, version 1.1},
}

@article{Topalidou:2015,
  author =       {Topalidou, Meropi and Rougier, Nicolas P.},
  title =        {{[Re] Interaction between cognitive and motor cortico-basal
                  ganglia loops during decision making: a computational study}},
  journal =      {ReScience},
  year =         2015,
  volume =       1,
  number =       1,
  doi =          {10.5281/zenodo.27944},
}

@article{Rougier:2017,
  doi =          {10.7717/peerj-cs.142},
  author =       {Nicolas P. Rougier and Konrad Hinsen and Frédéric Alexandre
                  and Thomas Arildsen and Lorena Barba and Fabien
                  C. Y. Benureau and C. Titus Brown and Pierre de Buyl and Ozan
                  Caglayan and Andrew P. Davison and Marc André Delsuc and
                  Georgios Detorakis and Alexandra K. Diem and Damien Drix and
                  Pierre Enel and Benoît Girard and Olivia Guest and Matt
                  G. Hall and Rafael Neto Henriques and Xavier Hinaut and Kamil
                  S Jaron and Mehdi Khamassi and Almar Klein and Tiina Manninen
                  and Pietro Marchesi and Dan McGlinn and Christoph Metzner and
                  Owen L. Petchey and Hans Ekkehard Plesser and Timothée Poisot
                  and Karthik Ram and Yoav Ram and Etienne Roesch and Cyrille
                  Rossant and Vahid Rostami and Aaron Shifman and Joseph
                  Stachelek and Marcel Stimberg and Frank Stollmeier and
                  Federico Vaggi and Guillaume Viejo and Julien Vitay and Anya
                  Vostinar and Roman Yurchak and Tiziano Zito},
  title =        {{Sustainable computational science: the ReScience
                  initiative}},
  journal =      {{PeerJ} Computer Science},
  month =        12,
  volume =       3,
  pages =        {e142},
  year =         2017,
  github =       {https://github.com/ReScience/ReScience-article-2},
  keywords =     {journal},
  tags =         {OS},
}

@article{Sinha:2022,
  author = {Sinha, Koustuv and Dodge, Jesse and Luccioni, Sasha and Forde, Jessica Zosa and Raparthy, Sharath Chandra and Pineau, Joelle and Stojnic, Robert},
  title = {{ML Reproducibility Challenge 2021}},
  journal = {ReScience C},
  year = {2022},
  month = may,
  volume = {8},
  number = {2},
  pages = {{\#48}},
  doi = {10.5281/zenodo.6574723},
  url = {https://zenodo.org/record/6574723/files/article.pdf},
  code_url = {},
  code_doi = {},
  code_swh = {},
  data_url = {},
  data_doi = {},
  review_url = {},
  type = {Editorial},
  language = {Python},
  domain = {ML Reproducibility Challenge 2021},
  keywords = {rescience c, machine learning, deep learning, python, pytorch}
}

@article{Sinha:2021,
  author = {Sinha, Koustuv and Dodge, Jesse and Luccioni, Sasha and Forde, Jessica Zosa and Stojnic, Robert and Pineau, Joelle},
  title = {{ML Reproducibility Challenge 2020}},
  journal = {ReScience C},
  year = {2021},
  month = may,
  volume = {7},
  number = {2},
  pages = {{\#1}},
  doi = {10.5281/zenodo.4833117},
  url = {https://zenodo.org/record/4833117/files/article.pdf},
  code_url = {},
  code_doi = {},
  code_swh = {},
  data_url = {},
  data_doi = {},
  review_url = {},
  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, neurips, reproducibility challenge}
}

@article{Sinha:2020,
  author = {Sinha, Koustuv and Pineau, Joelle and Forde, Jessica and Ke, Rosemary Nan and Larochelle, Hugo},
  title = {{NeurIPS 2019 Reproducibility Challenge}},
  journal = {ReScience C},
  year = {2020},
  month = may,
  volume = {6},
  number = {2},
  pages = {{\#11}},
  doi = {10.5281/zenodo.3818627},
  url = {https://zenodo.org/record/3818627/files/article.pdf},
  code_url = {},
  code_doi = {},
  code_swh = {},
  data_url = {},
  data_doi = {},
  review_url = {},
  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, neurips, reproducibility challenge}
}

@article{Pineau:2019,
  author = {Pineau, Joelle and Sinha, Koustuv and Fried, Genevieve and Ke, Rosemary Nan and Larochelle, Hugo},
  title = {{ICLR Reproducibility Challenge 2019}},
  journal = {ReScience C},
  year = {2019},
  month = may,
  volume = {5},
  number = {2},
  pages = {{\#5}},
  doi = {10.5281/zenodo.3158244},
  url = {https://zenodo.org/record/3158244/files/article.pdf},
  code_url = {},
  code_doi = {},
  data_url = {},
  data_doi = {},
  review_url = {https://github.com/ReScience/submissions/issues/5},
  type = {Editorial},
  language = {},
  domain = {},
  keywords = {machine learning, ICLR, reproducibility challenge}
}